Physicochemical Characterization of Anionic and Cationic Microemulsions: Water Solubilization, Particle Size Distribution, Surface Tension, and Structural Parameters
Bibliographic record
Abstract
The composition, oil type, and thermodynamic parameters influence the water solubilization capacity, particle size distribution, surface tension, and also the structural parameters of microemulsion systems. In the present study sodium lauryl sulfate (SLS) and hexadecyltrimethylammonium bromide (HTAB) have been used as surfactants and 3-methyl-1-butanol as cosurfactant to prepare microemulsions. Four n -alkanes (hexane, heptane, decane, and dodecane) were chosen as oil phases. Water solubilization capacities of anionic (SLS) and cationic (HTAB) microemulsions were investigated in both the presence and the absence of NaCl salt. A detailed study of particle size analysis for both the surfactants with different composition has been made from laser light scattering measurement. Surface tensions of microemulsions and surfactant solutions were also measured for investigation of their surface activities. Surface tensions have been reduced remarkably in the case of microemulsion systems compared to simple aqueous solutions of surfactants. Different structural parameters like water droplet and effective microemulsion droplet size including interface, aggregation numbers of surfactant, and cosurfactant have been determined assuming monodispersity of the droplets from dilution experiment. The effects of temperature on the above parameters have also been studied.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".